
"""simple docstring"""


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE			  =  16
SCREAMING_SNAKE_CASE			  =  32



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_ = 16					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		AutoTokenizer.from_pretrained("bert-base-cased"					)
       UpperCamelCase       			=		load_dataset("glue"		,   "mrpc"					)

       def tokenize_function(UpperCAmelCase_					):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase       			=		tokenizer(examples["sentence1"]		,   examples["sentence2"]		,   truncation=UpperCAmelCase_		,   max_length=UpperCAmelCase_					)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCamelCase       			=		datasets.map(
                  UpperCAmelCase_		,   batched=UpperCAmelCase_		,   remove_columns=["idx", "sentence1", "sentence2"]		,   )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCamelCase       			=		tokenized_datasets.rename_column("label"		,   "labels"					)

       def collate_fn(UpperCAmelCase_					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCamelCase       			=		1_28 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase       			=		16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase       			=		8
              else:
                     UpperCamelCase       			=		None

              return tokenizer.pad(
                  UpperCAmelCase_		,   padding="longest"		,   max_length=UpperCAmelCase_		,   pad_to_multiple_of=UpperCAmelCase_		,   return_tensors="pt"		,   )

       # Instantiate dataloaders.
       UpperCamelCase       			=		DataLoader(
           tokenized_datasets["train"]		,   shuffle=UpperCAmelCase_		,   collate_fn=UpperCAmelCase_		,   batch_size=UpperCAmelCase_					)
       UpperCamelCase       			=		DataLoader(
           tokenized_datasets["validation"]		,   shuffle=UpperCAmelCase_		,   collate_fn=UpperCAmelCase_		,   batch_size=UpperCAmelCase_					)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      SCREAMING_SNAKE_CASE			  =  mocked_dataloaders  # noqa: F811



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"		,   UpperCAmelCase_					) == "1":
              UpperCamelCase       			=		2
       # New Code #
       UpperCamelCase       			=		int(args.gradient_accumulation_steps					)
       # Initialize accelerator
       UpperCamelCase       			=		Accelerator(
           cpu=args.cpu		,   mixed_precision=args.mixed_precision		,   gradient_accumulation_steps=UpperCAmelCase_					)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"					)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase       			=		config["lr"]
       UpperCamelCase       			=		int(config["num_epochs"]					)
       UpperCamelCase       			=		int(config["seed"]					)
       UpperCamelCase       			=		int(config["batch_size"]					)

       UpperCamelCase       			=		evaluate.load("glue"		,   "mrpc"					)

       set_seed(UpperCAmelCase_					)
       UpperCamelCase			,							UpperCamelCase       			=		get_dataloaders(UpperCAmelCase_		,   UpperCAmelCase_					)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase       			=		AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,   return_dict=UpperCAmelCase_					)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCamelCase       			=		model.to(accelerator.device					)

       # Instantiate optimizer
       UpperCamelCase       			=		AdamW(params=model.parameters()		,   lr=UpperCAmelCase_					)

       # Instantiate scheduler
       UpperCamelCase       			=		get_linear_schedule_with_warmup(
           optimizer=UpperCAmelCase_		,   num_warmup_steps=1_00		,   num_training_steps=(len(UpperCAmelCase_					) * num_epochs)		,   )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		accelerator.prepare(
           UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       # Now we train the model
       for epoch in range(UpperCAmelCase_					):
              model.train()
              for step, batch in enumerate(UpperCAmelCase_					):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device					)
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(UpperCAmelCase_					):
                            UpperCamelCase       			=		model(**UpperCAmelCase_					)
                            UpperCamelCase       			=		output.loss
                            accelerator.backward(UpperCAmelCase_					)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(UpperCAmelCase_					):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device					)
                     with torch.no_grad():
                            UpperCamelCase       			=		model(**UpperCAmelCase_					)
                     UpperCamelCase       			=		outputs.logits.argmax(dim=-1					)
                     UpperCamelCase			,							UpperCamelCase       			=		accelerator.gather_for_metrics((predictions, batch["labels"])					)
                     metric.add_batch(
                         predictions=UpperCAmelCase_		,   references=UpperCAmelCase_		,   )

              UpperCamelCase       			=		metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"epoch {epoch}:"		,   UpperCAmelCase_					)



def 						lowerCamelCase__      (				)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser(description="Simple example of training script."					)
       parser.add_argument(
           "--mixed_precision"		,   type=UpperCAmelCase_		,   default=UpperCAmelCase_		,   choices=["no", "fp16", "bf16", "fp8"]		,   help="Whether to use mixed precision. Choose"
           "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
           "and an Nvidia Ampere GPU."		,   )
       # New Code #
       parser.add_argument(
           "--gradient_accumulation_steps"		,   type=UpperCAmelCase_		,   default=1		,   help="The number of minibatches to be ran before gradients are accumulated."		,   )
       parser.add_argument("--cpu"		,   action="store_true"		,   help="If passed, will train on the CPU."					)
       UpperCamelCase       			=		parser.parse_args()
       UpperCamelCase       			=		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCAmelCase_		,   UpperCAmelCase_					)


if __name__ == "__main__":
      main()







"""simple docstring"""


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE			  =  """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE			  =  direct_transformers_import(PATH_TO_TRANSFORMERS)

SCREAMING_SNAKE_CASE			  =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE			  =  {
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F"config.{attribute}" in modeling_source
                         or F"getattr(config, \"{attribute}\"" in modeling_source
                         or F"getattr(self.config, \"{attribute}\"" in modeling_source
                     ):
                            UpperCamelCase       			=		True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             RF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""		,   UpperCAmelCase_		,   )
                         is not None
                     ):
                            UpperCamelCase       			=		True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   UpperCamelCase       			=		True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       UpperCamelCase       			=		[
           "bos_index",
           "eos_index",
           "pad_index",
           "unk_index",
           "mask_index",
           "image_size",
           "use_cache",
           "out_features",
           "out_indices",
       ]
       UpperCamelCase       			=		["encoder_no_repeat_ngram_size"]

       # Special cases to be allowed
       UpperCamelCase       			=		True
       if not attribute_used:
              UpperCamelCase       			=		False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            UpperCamelCase       			=		True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            UpperCamelCase       			=		True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            UpperCamelCase       			=		True
                     elif attribute.endswith("_token_id"					):
                            UpperCamelCase       			=		True

                     # configuration class specific cases
                     if not case_allowed:
                            UpperCamelCase       			=		SPECIAL_CASES_TO_ALLOW.get(config_class.__name__		,   []					)
                            UpperCamelCase       			=		allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		dict(inspect.signature(config_class.__init__					).parameters					)
       UpperCamelCase       			=		[x for x in list(signature.keys()					) if x not in ["self", "kwargs"]]
       UpperCamelCase       			=		[signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       UpperCamelCase       			=		{}
       if len(config_class.attribute_map					) > 0:
              UpperCamelCase       			=		{v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       UpperCamelCase       			=		inspect.getsourcefile(UpperCAmelCase_					)
       UpperCamelCase       			=		os.path.dirname(UpperCAmelCase_					)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       UpperCamelCase       			=		[os.path.join(UpperCAmelCase_		,   UpperCAmelCase_					) for fn in os.listdir(UpperCAmelCase_					) if fn.startswith("modeling_"					)]

       # Get the source code strings
       UpperCamelCase       			=		[]
       for path in modeling_paths:
              if os.path.isfile(UpperCAmelCase_					):
                     with open(UpperCAmelCase_					) as fp:
                            modeling_sources.append(fp.read()					)

       UpperCamelCase       			=		[]
       for config_param, default_value in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              # `attributes` here is all the variant names for `config_param`
              UpperCamelCase       			=		[config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]					)

              if not check_attribute_being_used(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					):
                     unused_attributes.append(attributes[0]					)

       return sorted(UpperCAmelCase_					)



def 						lowerCamelCase__      (				)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       for _config_class in list(CONFIG_MAPPING.values()					):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              UpperCamelCase       			=		[
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class					)		,   lambda UpperCAmelCase_					: inspect.isclass(UpperCAmelCase_					)
                      and issubclass(UpperCAmelCase_		,   UpperCAmelCase_					)
                      and inspect.getmodule(UpperCAmelCase_					) == inspect.getmodule(_config_class					)		,   )
              ]
              for config_class in config_classes_in_module:
                     UpperCamelCase       			=		check_config_attributes_being_used(UpperCAmelCase_					)
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		unused_attributes

       if len(UpperCAmelCase_					) > 0:
              UpperCamelCase       			=		"The following configuration classes contain unused attributes in the corresponding modeling files:\n"
              for name, attributes in configs_with_unused_attributes.items():
                     error += F"{name}: {attributes}\n"

              raise ValueError(UpperCAmelCase_					)


if __name__ == "__main__":
      check_config_attributes()





